run.py

## initialize
from __future__ import print_function, division 
import torch.optim as optim
import torch.nn as nn
import data_loader
import iter_utils
import torch.utils.data as data_utils
from models import *

def main():
    ## load data
    # data_arr_01 = data_loader.load_data(r'toydata/data.txt')
    # label_vec = data_loader.load_label(r'toydata/label.txt')

    data_arr_01 = data_loader.load_data(r'data/uestc_pgb/SF01/vib_data_1.txt')
    # data_arr_03 = data_loader.load_data('data/pgb/SF03/vib_data_1.txt')
    # data_arr_01 = data_loader.resample_arr(data_arr_01, num=240) # add for Ince's model
    # data_arr_03 = data_loader.resample_arr(data_arr_03, num=240) # add for Ince's model
    # data_arr_01, _ = data_loader.fft_arr(data_arr_01) # add for fft wdcnn
    # data_arr_03, _ = data_loader.fft_arr(data_arr_03) # add for fft wdcnn
    # data_arr_01 = data_loader.stft_arr(data_arr_01) # add for stft-LeNet
    # data_arr_03 = data_loader.stft_arr(data_arr_03)
    label_vec = data_loader.load_label(r'data/uestc_pgb/SF01/label_vec.txt')

    trainset_01, testset_01 = data_loader.split_set(data_arr_01, label_vec)
    # trainset_03, testset_03 = data_loader.split_set(data_arr_03, label_vec)
    train_loader = data_utils.DataLoader(dataset = trainset_01, batch_size =512 , shuffle = True, num_workers = 2)
    test_loader = data_utils.DataLoader(dataset = testset_01, batch_size = 512, shuffle = True, num_workers = 2)
    print('Number of training samples: {}'.format(len(train_loader.dataset)))
    print('Number of testing samples: {}'.format(len(test_loader.dataset)))
    print( )

    ## make models
    model = wdcnn.Net(1, 5)

    ## train
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), weight_decay=0.0001)
    best_model, loss_curve = iter_utils.train(model, train_loader, criterion, optimizer,
        init_lr=0.0001, decay_epoch=5, n_epoch=10, use_cuda=False)

    # test
    test_accuracy = iter_utils.test(best_model, test_loader)
    print('Test accuracy: {:.4f}%'.format(100*test_accuracy))


    ## visualization
    # TODO

if __name__ == '__main__':
    main()